Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/92572
Type: Artigo de evento
Title: Nonlinear Blind Source Deconvolution Using Recurrent Prediction-error Filters And An Artificial Immune System
Author: Wada C.
Consolaro D.M.
Ferrari R.
Suyama R.
Attux R.
Von Zuben F.J.
Abstract: In this work, we propose a general framework for nonlinear prediction-based blind source deconvolution that employs recurrent structures (multi-layer perceptrons and an echo state network) and an immune-inspired optimization tool. The paradigm is tested under different channel models and, in all cases, the presence of feedback loops is shown to be a relevant factor in terms of performance. These results open interesting perspectives for dealing with classical problems such as equalization and blind source separation. © Springer-Verlag Berlin Heidelberg 2009.
Editor: 
Rights: fechado
Identifier DOI: 10.1007/978-3-642-00599-2_47
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-67149123384&partnerID=40&md5=8fb6cdf98bd11dad8c949514fadb2eef
Date Issue: 2009
Appears in Collections:Unicamp - Artigos e Outros Documentos

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